Facial recognition systems have proved useful in a variety of fields. Facial recognition has played a role in biometric security. For example, an automatic teller machine user's identification could be confirmed by capturing a real-time image of an individual as they withdraw cash from an account and comparing it to an image on record. Law enforcement has on occasion used facial recognition systems to identify wanted persons by capturing facial images in public crowds and comparing them against images in a database of wanted persons.
Although useful, current methods of facial recognition leave room for improvement. Variants across facial images, such as head tilt, illumination, and expression can negatively impact the precision of feature recognition, ultimately leading to failure in finding a match where the subject image is that of an individual that is indeed enrolled in a sample group or database.
Additionally, many methods aimed at improving facial identification success rates are implemented at the expense of efficiency. For example, precision could be improved immensely by analyzing every pixel of every image or applying complex normalizing algorithms to images. However, tasks such as these may be computationally expensive and slow the facial recognition process.